1. Cell Biology
  2. Immunology and Inflammation

Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells

  1. Moosung Lee
  2. Young-Ho Lee
  3. Jinyeop Song
  4. Geon Kim
  5. YoungJu Jo
  6. HyunSeok Min
  7. Chan Hyuk Kim  Is a corresponding author
  8. YongKeun Park  Is a corresponding author
  1. Korea Advanced Institute of Science and Technology, Republic of Korea
  2. Tomocube Inc, Republic of Korea
https://doi.org/10.7554/eLife.49023
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Cite this article
  1. Moosung Lee
  2. Young-Ho Lee
  3. Jinyeop Song
  4. Geon Kim
  5. YoungJu Jo
  6. HyunSeok Min
  7. Chan Hyuk Kim
  8. YongKeun Park
(2020)
Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells
eLife 9:e49023.
https://doi.org/10.7554/eLife.49023
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Abstract

The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.

Data availability

We have provided pre-processing and post-processing codes, and training and validation datasets used in Figure 3-Video 1 (https://osf.io/9w32p/). Also, the Unet architecture code is available in https://github.com/JinyeopSong/190124_CART-Segmentation-best.

The following data sets were generated

Article and author information

Author details

  1. Moosung Lee

    Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Competing interests
    Moosung Lee, Mr. Moosung Lee has financial interests in Tomocube Inc., a company that commercializes optical diffraction tomography and quantitative phase-imaging instruments, and is one of the sponsors of the work..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2826-5401

  2. Young-Ho Lee

    Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Competing interests
    Young-Ho Lee, Dr. Y.H. Lee is an employee of Curocell Inc.

  3. Jinyeop Song

    Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Competing interests
    No competing interests declared.

  4. Geon Kim

    Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Competing interests
    No competing interests declared.

  5. YoungJu Jo

    Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Competing interests
    No competing interests declared.

  6. HyunSeok Min

    Tomocube Inc, Daejeon, Republic of Korea
    Competing interests
    No competing interests declared.

  7. Chan Hyuk Kim

    Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    For correspondence
    kimchanhyuk@kaist.ac.kr
    Competing interests
    Chan Hyuk Kim, Prof. C. H. K. is a co-founder and shareholder of Curocell inc...

  8. YongKeun Park

    Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    For correspondence
    yk.park@kaist.ac.kr
    Competing interests
    YongKeun Park, Prof. Park has financial interests in Tomocube Inc., a company that commercializes optical diffraction tomography and quantitative phase-imaging instruments, and is one of the sponsors of the work.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0528-6661

Funding

National Research Foundation of Korea (2017M3C1A3013923)

  • Moosung Lee
  • Jinyeop Song
  • Geon Kim
  • YongKeun Park

National Research Foundation of Korea (2015R1A3A2066550)

  • Moosung Lee
  • Jinyeop Song
  • Geon Kim
  • YongKeun Park

National Research Foundation of Korea (2018K000396)

  • Moosung Lee
  • Jinyeop Song
  • Geon Kim
  • YongKeun Park

The Ministry of Science and ICT (2014M3A9D8032525)

  • Young-Ho Lee
  • Chan Hyuk Kim

The Ministry of Science and ICT (N11190028)

  • Young-Ho Lee
  • Chan Hyuk Kim

National Research Foundation of Korea (2019R1A2C1004129)

  • Young-Ho Lee
  • Chan Hyuk Kim

The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Michael L Dustin, University of Oxford, United Kingdom

Version history

  1. Received: June 4, 2019
  2. Accepted: December 16, 2020
  3. Accepted Manuscript published: December 17, 2020 (version 1)
  4. Version of Record published: January 20, 2021 (version 2)

Copyright

© 2020, Lee et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Moosung Lee
  2. Young-Ho Lee
  3. Jinyeop Song
  4. Geon Kim
  5. YoungJu Jo
  6. HyunSeok Min
  7. Chan Hyuk Kim
  8. YongKeun Park
(2020)
Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells
eLife 9:e49023.
https://doi.org/10.7554/eLife.49023

Share this article

https://doi.org/10.7554/eLife.49023

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